Podcasts about GraphQL

Our beloved former host, Jay Bell, stopped by this week to share what he's learned about using GraphQL to speed up development and keep code quality on point. Always a blast having him back!https://graphql.org/learn/https://www.apollographql.com/https://the-guild.dev/More about Jay: Bluesky: @jaycooperbell.dev‬LinkedIn: Jay BellX: @JayCooperBelltrellis.orghttps://graphql.org/learn/https://www.apollographql.com/https://the-guild.dev/ Follow us onX: The Angular Plus ShowBluesky: @theangularplusshow.bsky.social  The Angular Plus Show is a part of ng-conf. ng-conf is a multi-day Angular conference focused on delivering the highest quality training in the Angular JavaScript framework. Developers from across the globe converge  every year to attend talks and workshops by the Angular team and community experts.Join: http://www.ng-conf.org/Attend: https://ti.to/ng-conf/2025Follow: https://twitter.com/ngconf             https://www.linkedin.com/company/ng-conf             https://bsky.app/profile/ng-conf.bsky.social             https://www.facebook.com/ngconfofficialRead: https://medium.com/ngconfWatch: https://www.youtube.com/@ngconfonline  Edited by Patrick Hayes https://www.spoonfulofmedia.com/ Stock media provided by JUQBOXMUSIC/ Pond5

Send us a textCybersecurity vulnerabilities continue to emerge in unexpected places, as evidenced by the recent Iranian-backed attacks on U.S. water treatment facilities through poorly secured Unitronics PLCs. This alarming development sets the stage for our deep dive into API security - a critical yet often overlooked aspect of modern cybersecurity strategy.APIs form the connective tissue of our digital world, enabling seamless communication between different software systems. However, this interconnectivity creates numerous potential entry points for attackers. From RESTful APIs with their statelessness to enterprise-focused SOAP protocols and the newer GraphQL systems, each implementation brings unique security challenges that must be addressed proactively.We explore the most common API security threats facing organizations today: injection attacks that exploit poorly coded interfaces, broken authentication mechanisms that enable unauthorized access, sensitive data exposure through improper configurations, and man-in-the-middle attacks that intercept communications. Understanding these threats is just the beginning - implementing robust countermeasures is where real security happens.Authentication and access controls form the foundation of API security. OAuth, OpenID Connect, and token-based authentication systems provide powerful protection when implemented correctly. However, token management practices - including secure storage, proper revocation procedures, and regular refreshing - are equally critical yet frequently overlooked components of a comprehensive security strategy.API gateways emerge as perhaps the most valuable security control in your arsenal. Acting as centralized checkpoints, they provide enhanced visibility, consistent authentication enforcement, traffic throttling capabilities, and simplified management across numerous API connections. Cloud-based API gateways from major providers offer scalability and robust features that on-premises solutions struggle to match.Beyond the technical controls, we discuss the human element of API security. The most secure implementations balance protection with functionality while fostering collaboration between security professionals and developers. As I emphasize throughout the episode, effective security isn't about forcing compliance - it's about building bridges of understanding between teams with different expertise.Ready to strengthen your API security posture or prepare for your CISSP exam? Visit cisspcybertraining.com for free questions, comprehensive courseware, and a proven blueprint for certification success.Gain exclusive access to 360 FREE CISSP Practice Questions delivered directly to your inbox! Sign up at FreeCISSPQuestions.com and receive 30 expertly crafted practice questions every 15 days for the next 6 months—completely free! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Jack Herrington, podcaster, software engineer, writer and YouTuber, joins the pod to uncover the truth behind server functions and why they don't actually exist in the web platform. We dive into the magic behind frameworks like Next.js, TanStack Start, and Remix, breaking down how server functions work, what they simplify, what they hide, and what developers need to know to build smarter, faster, and more secure web apps. Links YouTube: https://www.youtube.com/@jherr Twitter: https://x.com/jherr Github: https://github.com/jherr ProNextJS: https://www.pronextjs.dev Discord: https://discord.com/invite/KRVwpJUG6p LinkedIn: https://www.linkedin.com/in/jherr Website: https://jackherrington.com Resources Server Functions Don't Exist (It Matters) (https://www.youtube.com/watch?v=FPJvlhee04E) We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Jack Herrington.

Whether or not you work in a technical role, you've probably heard of APIs! But what exactly are APIs? In this episode, we explain what an API is, what they are used for, and how HTTP comes into it. Finally, we finish up by giving a brief overview of the different API protocols out there - from REST, to SOAP, to GraphQL! And in true Hot Girls Code style, we bring the tech to life with fun, relatable analogies featuring two of our favourite spots: restaurants and makeup stores.New episodes come out fortnightly on Wednesday morning (NZT).Where to Find Us: ⁠Instagram⁠ ⁠Tik Tok The Hot Girls Code Website⁠Sponsored by:Trade Me

Guests Ben Nickolls | Andrew Nesbitt Panelist Richard Littauer Show Notes In this episode of Sustain, host Richard is joined by guests Ben Nickolls and Andrew Nesbitt to discuss the ecosyste.ms project. They explore how ecosyste.ms collects and analyzes metadata from various open-source projects to create a comprehensive database that can help improve funding allocation. The discussion covers the importance of funding the most critical open-source projects, the existing gaps in funding, and the partnership between ecosyste.ms and Open Source Collective to create funding algorithms that support entire ecosystems. They also talk about the challenges of maintaining data, reaching out to project maintainers, and the broader implications for the open-source community. Hit the download button now! [00:01:58] Andrew and Ben explain ecosyste.ms, what it does, and how it compares to Libraries.io. [00:04:59] Ecosyste.ms tracks metadata, not the packages themselves, and enriches data via dependency graphs, committers, issues, SBOMs, and more. [00:06:54] Andrew talks about finding 1,890 Git hosts and how many critical projects live outside GitHub. [00:08:37] There's a conversation on metadata uses and SBOM parsing. [00:12:49] Richard inquires about the ecosystem.ms funds on their website which Andrew explains it's a collaboration between Open Collective and ecosyste.ms. that algorithmically distributes funds to the most used, not most popular packages. [00:15:45] Ben shares how this is different from previous projects and brings up a past project, “Back Your Stack” and explains how ecosyste.ms is doing two things differently. [00:18:59] Ben explains how it supports payouts to other platforms and encourages maintainers to adopt funding YAML files for automation. Andrew touches on efficient outreach, payout management, and API usage (GraphQL). [00:25:36] Ben elaborates on how companies can fund ecosyste.ms (like Django) instead of curating their own lists and being inspired by Sentry's work with the Open Source Pledge. [00:29:32] Andrew speaks about scaling and developer engagement and emphasizes their focus is on high-impact sustainability. [00:32:48] Richard asks, “Why does it matter?” Ben explains that most current funding goes to popular, not most used projects and ecosyste.ms aims to fix the gap with data backed funding, and he suggests use of open standards like 360Giving and Open Contracting Data. [00:35:46] Andrew shares his thoughts on funding the right projects by improving 1% of OSS, you uplift the quality of millions of dependent projects with healthier infrastructure, faster security updates, and more resilient software. [00:38:35] Find out where you can follow ecosyste.ms and the blog on the web. Quotes [00:11:18] “I call them interesting forks. If a fork is referenced by a package, it'll get indexed.” [00:22:07] We've built a service that now moves like $25 million a year between OSS maintainers on OSC.” [00:33:23] “We don't have enough information to make collective decisions about which projects, communities, maintainers, should receive more funding.” [00:34:23] “The NSF POSE Program has distributed hundreds of millions of dollars of funding to open source communities alone.” [00:35:47] “If you have ten, twenty thousand really critical open source projects, that actually isn't unachievable to make those projects sustainable.” Spotlight [00:39:35] Ben's spotlight is Jellyfin. [00:40:20] Andrew's spotlight is zizmor. [00:42:21] Richard's spotlight is The LaTeX Project. Links SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) richard@sustainoss.org (mailto:richard@sustainoss.org) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) SustainOSS Bluesky (https://bsky.app/profile/sustainoss.bsky.social) SustainOSS LinkedIn (https://www.linkedin.com/company/sustainoss/) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Ben Nickolls LinkedIn (https://www.linkedin.com/in/benjamuk/) Andrew Nesbitt Website (https://nesbitt.io/) Andrew Nesbitt Mastodon (https://mastodon.social/@andrewnez) Octobox (https://github.com/octobox) ecosyste.ms (https://ecosyste.ms/) ecosyste.ms Blog (https://blog.ecosyste.ms/) Open Source Collective (https://oscollective.org/) Open Source Collective Updates (https://opencollective.com/opensource/updates) Open Source Collective Contributions (https://opencollective.com/opensource) Open Source Collective Contributors (https://opencollective.com/open-source) Open Collective (https://opencollective.com/) 24 Pull Requests (https://24pullrequests.com/) Libraries.io (https://libraries.io/) The penumbra of open source (EPJ Data Science) (https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-022-00345-7) FOSDEM '25- Open source funding: you're doing it wrong (Andrew and Ben) (https://fosdem.org/2025/schedule/event/fosdem-2025-5576-open-source-funding-you-re-doing-it-wrong/) Vue.js (https://vuejs.org/) thanks.dev (https://thanks.dev/home) StackAid (https://www.stackaid.us/) Back Your Stack (https://backyourstack.com/) NSF POSE (https://www.nsf.gov/funding/initiatives/pathways-enable-open-source-ecosystems) Django (https://www.djangoproject.com/) GitHub Sponsors (https://github.com/sponsors) Sustain Podcast-Episode 80: Emma Irwin and the Foss Fund Program (https://podcast.sustainoss.org/80) Sustain Podcast- 3 Episodes featuring Chad Whitacre (https://podcast.sustainoss.org/guests/chad-whitacre) Sustain Podcast- Episode 218: Karthik Ram & James Howison on Research Software Visibility Infrastructure Priorities (https://podcast.sustainoss.org/218) Sustain Podcast-Episode 247: Chad Whitacre on the Open Source Pledge (https://podcast.sustainoss.org/247) Invest in Open Infrastructure (https://investinopen.org/) 360Giving (https://www.360giving.org/) Open Contracting Data Standard (https://standard.open-contracting.org/latest/en/) Jellyfin (https://opencollective.com/jellyfin) zizmor (https://github.com/zizmorcore/zizmor) The LaTeX Project (https://www.latex-project.org/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Andrew Nesbitt and Benjamin Nickolls.

CTO and co-founder of Apollo, Matt DeBergalis, joins us on this episode to talk about how GraphQL has continued to evolve over time, and how Apollo is focused on making it more accessible for developers and AI agents than ever before.For those less familiar with Apollo and GraphQL, Matt shares the history of both, including lessons he learned from founding the company Meteor that helped him while building Apollo.We discuss how even though GraphQL is making less headlines than it was a few years ago, it's really begun to find its niche within larger organizations that have hundreds or even thousands of APIs and databases underpinning their many applications, and how Apollo has continued to evolve so that it can support APIs, serverless functions, and SQL- or no SQL-databases, with little extra code needed to make these different data sources work together.Matt also highlights the benefits of a GraphQL schema for AI agents and MCP servers, sharing how the agents are generally very good at parsing the schemas and understanding how to leverage queries against the interface to retrieve the data they need. While we've had tech stacks in the past like LAMP and MERN, this new addition of AI to the development mix provides a unique opportunity to redefine the stack once more, and GraphQL could be a very good piece to include.Special GuestMatt DeBergalis, CTO and co-founder of ApolloRelevant Links:Apollo GraphQL websiteApollo GraphQL YouTubeApollo GraphQL LinkedInApollo GraphQL on XMatt on GitHubMatt on XMatt on MediumMatt on LinkedInWhat Makes Us Happy this Week:Paige - The Pitt TV seriesTJ - I Think I Was MurderedMatt - Teaching my daughter to ride a bikeThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

Swimming against the current sometimes leads to unexpected treasures. In this fascinating conversation, Adam Fortuna reveals how migrating Hardcover—a social network for readers with 30,000 users—from Next.js back to Ruby on Rails delivered surprising performance improvements and development simplicity.The journey begins with Adam explaining how Hardcover originated as a response to Goodreads shutting down their API. As a longtime Rails developer who initially chose Next.js for its server-side rendering capabilities, Adam found himself drawn back to Rails once modern tools made it viable to combine Rails' backend strengths with React's frontend interactivity. The migration wasn't a complete rewrite—they preserved their React components while replacing GraphQL with ActiveRecord—and unexpectedly saw significant improvements in page load speeds and SEO rankings.At the heart of this technical evolution is Inertia.js, which Adam describes as "the missing piece for Rails for a long time." This elegant solution allows direct connections between Rails controllers and React components without duplicating routes, creating a seamless developer experience. We dive into the challenges they faced, particularly with generating Open Graph images and handling API abuse, and how they solved these problems with pragmatic hybrid approaches.The conversation takes an exciting turn as Adam discusses their work on book recommendation engines, combining collaborative filtering with content analysis to help readers discover their next favorite book. As someone currently enjoying the Dungeon Crawler Carl series (described as "RPG mixed with Hitchhiker's Guide"), Adam's passion for both books and elegant technical solutions shines throughout.Listen in as we explore how going against conventional wisdom sometimes leads to better outcomes, and discover why Hardcover is now being open-sourced to invite community collaboration. Whether you're interested in Rails, JavaScript frameworks, or book recommendations, this episode offers valuable insights into making technical decisions based on real-world results rather than following trends.Linkshttps://hardcover.app/blog/part-1-how-we-fell-out-of-love-with-next-js-and-back-in-love-with-ruby-on-rails-inertia-jshttps://adamfortuna.com/https://bsky.app/profile/adamfortuna.comSend us some love.HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleAutoscaling that actually works. Take control of your cloud hosting.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the show

Episode SummaryJeremy Snyder is the co-founder and CEO of FireTail, a company that enables organizations to adopt AI safely without sacrificing speed or innovation. In this conversation, Jeremy shares his deep expertise in API and AI security, highlighting the second wave of cloud adoption and his pivotal experiences at AWS during key moments in its growth from startup onwards.Show NotesIn this episode of The Secure Developer, host Danny Allan sits down with Jeremy Snyder, the Co-founder and CEO of FireTail, to unravel the complexities of API security and explore its critical intersection with the burgeoning field of Artificial Intelligence. Jeremy brings a wealth of experience, tracing his journey from early days in computational linguistics and IT infrastructure, through a pivotal period at AWS during its startup phase, to eventually co-founding FireTail to address the escalating challenges in API security driven by modern, decoupled software architectures.The conversation dives deep into the common pitfalls and crucial best practices for securing APIs. Jeremy clearly distinguishes between authentication (verifying identity) and authorization (defining permissions), emphasizing that failures in authorization are a leading cause of API-related data breaches. He sheds light on vulnerabilities like Broken Object-Level Authorization (BOLA), explaining how seemingly innocuous practices like using sequential integer IDs can expose entire datasets if server-side checks are missed. The discussion also touches on the discoverability of backend APIs and the persistent challenges surrounding multi-factor authentication, including the human element in security weaknesses like SIM swapping.Looking at current trends, Jeremy shares insights from FireTail's ongoing research, including their annual "State of API Security" report, which has uncovered novel attack vectors such as attempts to deploy malware via API calls. A significant portion of the discussion focuses on the new frontier of AI security, where APIs serve as the primary conduit for interaction—and potential exploitation. Jeremy details how AI systems and LLM integrations introduce new risks, citing a real-world example of how a vulnerability in an AI's web crawler API could be leveraged for DDoS attacks. He speculates on the future evolution of APIs, suggesting that technologies like GraphQL might become more prevalent to accommodate the non-deterministic and data-hungry nature of AI agents. Despite the evolving threats, Jeremy concludes with an optimistic view, noting that the gap between business adoption of new technologies and security teams' responses is encouragingly shrinking, leading to more proactive and integrated security practices.LinksFireTailRapid7Snyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

React Core team member Dan Abramov joins us to explore "JSX over the wire" and the evolving architecture of React Server Components. We dive into the shift from traditional REST APIs to screen-specific data shaping, the concept of Backend for Frontend (BFF), and why centering UI around the user experience—not server/client boundaries—matters more than ever. Links https://danabra.mov https://github.com/gaearon https://bsky.app/profile/danabra.mov https://overreacted.io https://www.youtube.com/@danabramov Resources JSX Over The Wire: https://overreacted.io/jsx-over-the-wire/ Impossible Components: https://overreacted.io/impossible-components/ What Does "use client" Do?: https://overreacted.io/what-does-use-client-do/ Our Journey With Caching: https://nextjs.org/blog/our-journey-with-caching https://parceljs.org https://nextjs.org/docs/app We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Dan Abramov.

Conosciamo Patrick Arminio, Founding Engineer presso FastAPI Labs, creatore di Strawberry e presidente di Python Italia. Iniziamo con una panoramica di GraphQL e REST API. Chiudiamo poi con la nuovissima FastAPI Labs, avventura imprenditoriale di Patrick Arminio e Sebastián Ramírez (tiangolo).

Join Pascal and Sabrina on the latest Meta Tech Podcast episode as they discuss the evolution and future of GraphQL. From client-side consistency to innovative APIs, learn how GraphQL is making developers' lives easier and enhancing user experiences. Discover surprising insights into the challenges of building a mobile GraphQL platform and how it's transforming product development at Meta.  Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links GraphQL: https://graphql.org/  Relay: https://relay.dev/  Sabrina at GraphQL Conf 2024: https://www.youtube.com/watch?v=PGBC-0E-kco  Timestamps Intro 0:06 Introduction Sabrina 1:42 Sabrina's team 2:47 What's GraphQL? 3:18 Relay and Mobile GraphQL Clients 4:01 GraphQL Consistency Engine 4:54 Pando Mobile GraphQL Client 7:16 Interfacing with Pando 8:03 Code generation 9:14 Inventing new features 10:43 The hidden complexity behind pagination 11:52 Working inside the GraphQL spec 16:00 Complexity tradeoffs 18:30 State of GraphQL at Meta 21:16 Measuring success 24:58 Optimistic Mutations 27:31 Collaboration model 31:42 Preventing early adoption 34:43 The challenge of migrating FBApp 37:10 What's next for mobile GraphQL? 40:22 Outro 41:54  

El próximo 21 de marzo, a las 19:30h, el Centro Cívico de Canido se convertirá en el punto de encuentro ideal para todos aquellos desarrolladores y marketers interesados en llevar sus sitios web al siguiente nivel. El taller "Revolucionando WordPress: Arquitectura Headless con Next.js, GraphQL y Vercel" promete ser una experiencia transformadora, donde los participantes podrán descubrir cómo modernizar sus sitios web basados en WordPress mediante una arquitectura headless. Néstor López, Platform Engineer, será el ponente encargado de guiar a los asistentes en este proceso de transformación, mostrando cómo hacer que WordPress sea más rápido, seguro y escalable mediante herramientas de vanguardia como Next.js, GraphQL y Vercel. El taller está dirigido tanto a desarrolladores como a profesionales del marketing digital, quienes podrán aprender cómo optimizar la estructura de sus webs para una mejor experiencia de usuario y un rendimiento superior. Como es habitual en estos eventos, al finalizar la parte técnica, los asistentes podrán disfrutar del esperado #MomentoNetworking, donde podrán relajarse, compartir experiencias y conocer a otros profesionales del sector mientras disfrutan de pinchos y cervezas, cortesía de los patrocinadores Raiola Networks.

Max Stoiber is Co-Founder & CEO ofStellate, the GraphQL edge platformrecently acquired by Shopify.In this episode, we discuss:The Stellate journey from idea to initial traction to acquisitionThe market size (and limitations) for GraphQL, APIs, and DevToolsHow he ran a top-notch acquisition process for StellateWhy startups fail

Karina Nguyen leads research at OpenAI, where she's been pivotal in developing groundbreaking products like Canvas, Tasks, and the o1 language model. Before OpenAI, Karina was at Anthropic, where she led post-training and evaluation work for Claude 3 models, created a document upload feature with 100,000 context windows, and contributed to numerous other innovations. With experience as an engineer at the New York Times and as a designer at Dropbox and Square, Karina has a rare firsthand perspective on the cutting edge of AI and large language models. In our conversation, we discuss:• How OpenAI builds product• What people misunderstand about AI model training• Differences between how OpenAI and Anthropic operate• The role of synthetic data in model development• How to build trust between users and AI models• Why she moved from engineering to research• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Vanta—Automate compliance. Simplify security• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/why-soft-skills-are-the-future-of-work-karina-nguyen—Where to find Karina Nguyen:• X: https://x.com/karinanguyen_• LinkedIn: https://www.linkedin.com/in/karinanguyen28• Website: https://karinanguyen.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Karina Nguyen(04:42) Challenges in model training(08:21) Synthetic data and its importance(12:38) Creating Canvas(18:33) Day-to-day operations at OpenAI(20:28) Writing evaluations(23:22) Prototyping and product development(26:57) Building Canvas and Tasks(33:34) Understanding the job of a researcher(35:36) The future of AI and its impact on work and education(42:15) Soft skills in the age of AI(47:50) AI's role in creativity and strategy development(53:34) Comparing Anthropic and OpenAI(57:11) Innovations and future visions(01:07:13) The potential of AI agents(01:11:36) Final thoughts and career advice—Referenced:• What's in your stack: The state of tech tools in 2025: https://www.lennysnewsletter.com/p/whats-in-your-stack-the-state-of• Anthropic: https://www.anthropic.com/• OpenAI: https://openai.com/• What is synthetic data—and how can it help you competitively?: https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively• GPQA: https://datatunnel.io/glossary/gpqa/• Canvas: https://openai.com/index/introducing-canvas/• Barret Zoph on LinkedIn: https://www.linkedin.com/in/barret-zoph-65990543/• Mira Murati on LinkedIn: https://www.linkedin.com/in/mira-murati-4b39a066/• JSON Schema: https://json-schema.org/• Anthropic—100K Context Windows: https://www.anthropic.com/news/100k-context-windows• Claude 3 Haiku: https://www.anthropic.com/news/claude-3-haiku• A.I. Chatbots Defeated Doctors at Diagnosing Illness: https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors-diagnosis.html• Cursor: https://www.cursor.com/• How AI will impact product management: https://www.lennysnewsletter.com/p/how-ai-will-impact-product-management• Lee Byron on LinkedIn: https://www.linkedin.com/in/lee-byron/• GraphQL: https://graphql.org/• Claude in Slack: https://www.anthropic.com/claude-in-slack• Sam Altman on X: https://x.com/sama• Jakub Pachocki on LinkedIn: https://www.linkedin.com/in/jakub-pachocki/• Lennybot: https://www.lennybot.com/• ElevenLabs: https://elevenlabs.io/• Westworld on Prime Video: https://www.amazon.com/Westworld-Season-1/dp/B01N05UD06• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Tuple: https://tuple.app/• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Today we are talking about GraphQL, Drupal Decoupled, and What to do with them with guest Jesus Manuel Olivas. We'll also cover CORS UI as our module of the week. For show notes visit: https://www.talkingDrupal.com/486 Topics What is GraphQL How do you use GraphQL with Drupal Would you use GraphQL without a headless theme Do you need additional server requirements What are some of your favorite GraphQL modules What caused the change from v3 to v4 What is meant by Drupal Decoupled What are the best use cases How do you handle caching and performance How do you handle roles and permissions Do you think AI has made decoupled more interesting Resources GraphQL GraphQL Compose GraphQL Compose Preview GraphQL Compose Webform GraphQL Compose Fragments Swagger UI Custom Field Drupal Decoupled Guests Jesus Manuel Olivas - drupal-decoupled.octahedroid.com jmolivas Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Weston - scott-weston MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to control your site's Cross-Origin Resource Sharing (aka CORS) configuration, directly within the Drupal admin UI? There's a module for that. Module name/project name: CORS UI Brief history How old: created in Sep 2016 by Sam Becker (sam152), a prolific module maintainer in his own right, though the most recent release is by Matt Glaman, who has been on this show and will need no introduction for many of our listeners Versions available: 8.x-1.2 which supports Drupal 9, 10, and 11 Maintainership Actively maintained Security coverage Number of open issues: 2 open issues, 1 of which is a bug, and also has a patch available Usage stats: 274 sites according to drupal.org Module features and usage By default cross-origin requests to Drupal applications will be denied. Since Drupal 8.2 you can add a section to your site's services.yml file to enable responses, and specify what headers, methods, and origins should be supported This module provides a form within Drupal to control these values. This could be helpful if, for example, these values need to change on a frequent basis, or for less technical users who are experimenting with a headless architecture. I should note that the bug mentioned earlier throws a fatal error in PHP 8, but is a simple fix. So if you want to try out this module, make sure you apply the patch.

Hoje o papo é sobre GraphQL no mobile. Neste episódio, conversamos sobre o histórico do GraphQL, desde os problemas que ele veio para resolver, até ecossistema, o que é (e o que não é) responsabilidade do GraphQL, vantagens e desvantagens do uso de GraphQL versus REST, e muito mais. Vem ver quem participou desse papo: André David, o host que já é o tradicional co-host Vinny Neves, Líder de Front-End na Alura Yago Oliveira, Coordenador de Conteúdo Técnico na Alura William Bezerra, Instrutor na Alura e Engenheiro Sênior no QuintoAndar

Søren Bramer Schmidt, co-founder and CEO of Prisma, joins us to discuss the journey of building one of the largest developer communities in DevTools. Søren shares how Prisma's deliberate strategies have shaped its growth, feature prioritization, and the launch of new products like Prisma Postgres. We also explore the challenges of managing a vast user base and how Prisma is adapting to shifts in application development.We discuss:How intentional partnerships with educators and influencers fueled Prisma's early growth.Strategies to engage the GraphQL community and gain visibility on platforms like Hacker News.Managing a large developer community while balancing innovation with stability.The evolution from Graphcool to Prisma ORM, including lessons from early pivots.Launching Prisma Postgres and how community feedback influenced its development.Implementing a simple, usage-based pricing model and reducing infrastructure costs through self-hosting.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. https://workos.com/Links:Prisma (https://www.prisma.io/)Prisma Postgres (https://www.prisma.io/postgres)Feldera (https://feldera.com/)

In this episode of Compressed FM, Dustin Goodman shares insights from his journey from IC to engineering manager at companies like ClickUp and This Dot. The conversation explores the nuances of technical leadership, team dynamics, and the importance of understanding personal values in management. The discussion then shifts to a deep dive into React Server Components, examining their implementation challenges and potential impact on the framework ecosystem. SponsorsWix Studio combines the best of both worlds—intuitive design tools for clients and full-stack flexibility for developers. Customize every detail with your own code and take control of your projects.Chapter Marks00:00:00 - Intro00:00:42 - Sponsor: Wix Studio00:01:33 - Engineering Management Journey00:05:11 - Managing Different Experience Levels00:07:14 - Technical Skills in Management00:09:27 - Should Managers Code?00:12:19 - Managing Up vs Managing Down00:17:27 - Team Values Discussion00:20:11 - Strengths and Management Styles00:26:07 - React Server Components Introduction00:29:27 - RSC Implementation Challenges00:34:34 - GraphQL and Server Components00:39:13 - Future of React Frameworks00:43:10 - Vite 6 Discussion00:47:52 - React Community Evolution00:51:21 - Picks and PlugsAmy Dutton:Pick: Browse AI (web scraping tool with AI capabilities)Plug: Advent of CSS and Advent of JavaScript (24 coding challenges in December)Dustin Goodman:Pick: Cursor (AI-powered code editor)Plug: "Engineering Management for the Rest of Us" by Sarah DrasnerBrad Garropy:Pick: Helldivers 2 (video game)Plug: Raycast extension for Stripe (automatically fills checkouts with test cards)01:00:14 - Show Wrap-upLinksBooks Mentioned:"The Manager's Path" by Camille Fournier"Engineering Management for the Rest of Us" by Sarah DrasnerTools & Software:Wix StudioBrowse AICursor (code editor)RaycastRaycast Stripe extensionVite 6Next.jsSocial/Community:BlueSky (Brad and Amy)Bytes NewsletterConnectTech conferencePeople Referenced:Ryan BurgessGergely OroszTracy LeeDan AbramovTanner LindsleyJohn LindquistDavid KhourshidAssessment Tools:Clifton StrengthsFinderAPIs/Documentation:Stripe test cards documentationReact Server Components documentationVite documentationProjects:Advent of CSS (adventofcss.com)Advent of JavaScript (adventofjs.com) 

In this special episode of Watson Weekly, Rick Watson is joined by Kelly Goetsch, a Commercetools Advisor and industry thought leader. Kelly shares his unique insights into the evolving landscape of e-commerce, focusing on the intersection of technology and healthcare. Together, they explore key topics like consumer behavior trends, the growing role of composable commerce, and the untapped opportunities in health tech. From tackling HIPAA compliance to redefining retail experiences, this episode dives deep into the transformative potential of technology across industries. Don't miss this engaging discussion packed with expertise and forward-thinking strategies.About Kelly - Kelly Goetsch is a commercetools Advisor. Until January 2025, Kelly was the company's Chief Strategy Officer, and prior to that, he served as the Chief Product Officer at commercetools for nearly six years. Goetsch is an industry thought-leader who champions the MACH (Microservices, API, Cloud-Native, and Headless) movement, and co-founded the MACH Alliance, a group of 100+ independent, future-thinking tech companies dedicated to advocating for open, best-of-breed technology ecosystems. Prior to commercetools, Goetsch held senior-level product development and go-to-market responsibilities at Oracle and held the role of Senior Architect ATG (acquired by Oracle), where he was instrumental to 31 large-scale ATG implementationsHe is the author of four books - GraphQL for Modern Commerce (O'Reilly, 2020), APIs for Modern Commerce (O'Reilly, 2017), Microservices for Modern Commerce (O'Reilly, 2016) and E-Commerce in the Cloud (O'Reilly, 2014). He holds three patents, including one key to distributed computing.

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World's Fair 2025 in June.Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!Full YouTube Episode with Slides/ChartsLike and subscribe and hit that bell to get notifs!Timestamps* 00:00 Welcome to the 100th Episode!* 00:19 Reflecting on the Journey* 00:47 AI Engineering: The Rise and Impact* 03:15 Latent Space Live and AI Conferences* 09:44 The Competitive AI Landscape* 21:45 Synthetic Data and Future Trends* 35:53 Creative Writing with AI* 36:12 Legal and Ethical Issues in AI* 38:18 The Data War: GPU Poor vs. GPU Rich* 39:12 The Rise of GPU Ultra Rich* 40:47 Emerging Trends in AI Models* 45:31 The Multi-Modality War* 01:05:31 The Future of AI Benchmarks* 01:13:17 Pionote and Frontier Models* 01:13:47 Niche Models and Base Models* 01:14:30 State Space Models and RWKB* 01:15:48 Inference Race and Price Wars* 01:22:16 Major AI Themes of the Year* 01:22:48 AI Rewind: January to March* 01:26:42 AI Rewind: April to June* 01:33:12 AI Rewind: July to September* 01:34:59 AI Rewind: October to December* 01:39:53 Year-End Reflections and PredictionsTranscript[00:00:00] Welcome to the 100th Episode![00:00:00] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host Swyx for the 100th time today.[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.[00:00:19] Alessio: Yeah, I know.[00:00:19] Reflecting on the Journey[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer[00:00:32] swyx: was cursor and perplexity.[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.[00:00:47] AI Engineering: The Rise and Impact[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.[00:03:14] swyx: Yeah.[00:03:15] Latent Space Live and AI Conferences[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.[00:05:06] swyx: I would say that I actually also created.[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll[00:09:30] Alessio: publish[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.[00:09:44] The Competitive AI Landscape[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.[00:13:11] swyx: Mm-hmm . Yeah. Maybe[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,[00:16:32] Alessio: and our friend, yeah.[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to[00:18:02] swyx: keep going. I think that 2, will come.[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.[00:18:45] Alessio: Yeah. They need to put that in Lindy.[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.[00:20:03] swyx: And so I'll highlight that talk as well.[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.[00:21:45] Synthetic Data and Future Trends[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.[00:23:37] Alessio: Yeah,[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.[00:25:46] Alessio: And then the post training slope is like the, the human one.[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.[00:26:05] Alessio: I don't[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.[00:26:07] Alessio: Yeah. Any other new reps? Highlights?[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really[00:26:20] swyx: nice.[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is[00:29:57] Alessio: talking[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only[00:33:17] Alessio: cage free annotated data is useful.[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.[00:33:32] swyx: I don't know if you have any perspectives there.[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,[00:34:17] swyx: they don't[00:34:17] Alessio: really work[00:34:18] swyx: on.[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?[00:35:53] Creative Writing with AI[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.[00:36:11] Alessio: Yeah.[00:36:12] Legal and Ethical Issues in AI[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00][00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.[00:38:18] The Data War: GPU Poor vs. GPU Rich[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.[00:39:12] The Rise of GPU Ultra Rich[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.[00:40:47] Emerging Trends in AI Models[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,[00:40:58] swyx: um,[00:40:58] swyx: uh, pretty aggressively.[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.[00:42:21] Alessio: Yeah, yeah,[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was[00:45:15] Alessio: 1B.[00:45:15] swyx: Did I say that?[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,[00:45:22] swyx: yeah.[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.[00:45:31] The Multi-Modality War[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I[00:45:54] swyx: think it's generally available now, you can go to Sora.[00:45:56] swyx: com and try it. Yeah, they had[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.[00:46:45] Alessio: Yeah, I mean,[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried[00:48:53] swyx: the same problems on black forest?[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid[00:49:06] swyx: journey. Call the API four times.[00:49:08] Alessio: No, but then there's no like variate.[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?[00:49:24] Alessio: Yeah,[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.[00:52:58] Alessio: So episode essays,[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some[00:53:4

I continue my conversation with Sophia Willows, the Head of Engineering at Rye, an a16z-backed developer tools startup making APIs for online commerce. At Rye, Sophia shapes the technical direction of the company and is responsible for building a high-performance engineering culture.In this part Sophia discusses the challenges of creating consistent and user-friendly APIs. She emphasizes the importance of involving developers early in the design process, using clear and consistent documentation, and leveraging tools like GraphQL to enforce structure and consistency.The conversation shifts to the impact of AI on the future of software development. While AI can automate certain tasks, it's unlikely to replace the need for human creativity and problem-solving skills. Sophia encourages developers to focus on higher-level thinking and domain expertise, which are areas where AI is less likely to make significant inroads.Finally, Sophia addresses the issue of burnout and the importance of finding a sustainable work-life balance. She suggests that developers should identify what energizes them and focus on those activities, whether they're technical or non-technical. By understanding their own motivations and setting realistic expectations, developers can thrive in the ever-evolving field of software engineering.Sophia Willows is the Head of Engineering at Rye, an a16z-backed developer tools startup making APIs for online commerce. At Rye, Sophia shapes the technical direction of the company and is responsible building a high-performance engineering culture.She previously worked in EdTech as Engineering Manager for Crimson Education's AI team. Designing and implementing their generative AI strategy, Sophia lead the development of solutions that enhanced the company's educational offerings and personalized student learning experiences. Just before she moved to Rye, Crimson secured a $1B market valuation.Sophia is a recognized figure in the tech community, frequently judging global hackathons and contributing to industry discussions through speaking engagements and blogging.Handles:* https://sophiabits.com/blog* https://www.linkedin.com/in/sophia-willows

In today's jam-packed episode, they dive deep into the world of API design, logging best practices, and effective configuration management. Our esteemed guests, Michael Dawson, James Snell, Matteo Collina, and Natalia Venditto, bring their extensive expertise to the table, discussing the nuances between GraphQL and REST/Open API, the merits of API First vs. Code First approaches, and the impacts of global states in Node.js applications.You'll hear insights on how to maintain effective API contracts, avoid common pitfalls in software development, and implement robust error handling and logging mechanisms. Additionally, the episode covers practical advice on optimizing large-scale ecosystems with tools like Pino and managing dependencies thoughtfully to avoid technical debt.They also touch on the personal side of development, with James Snell emphasizing the importance of well-being by taking regular breaks. Charles Max Wood shares his recent experience at a board game convention and recommends the TV show "Reacher" for some downtime entertainment.So, sit back and enjoy this enlightening conversation that spans across technical deep dives and light-hearted discussions, offering valuable takeaways for developers at all levels.SocialsLinkedIn: James SnellLinkedIn: Michael DawsonLinkedIn: Matteo CollinaLinkedIn: Natalia VendittoPicksCharles - Gnome Hollow | Board GameCharles - Reacher (TV Series 2022Michael - MakerWorld: Download Free 3D Printing Models Become a supporter of this podcast: https://www.spreaker.com/podcast/javascript-jabber--6102064/support.

In this conversation, Ben, a Director of Product at Shopify, shares his journey to the company, discusses the latest updates in Shopify Editions Winter '25, and highlights improvements in the CLI and GraphQL API. Ben emphasizes the importance of community engagement and the potential for future developments in the Shopify ecosystem. Ongoing improvements and future vision for Liquid and its integration with developer tools like VS Code are highlighted. Ben also emphasizes the importance of enhancing the developer experience, streamlining workflows, and leveraging community feedback. The discussion also touches on the role of AI in development, the significance of open-source collaboration, and the need for a cohesive and intuitive coding environment. *Episode Takeaways* - The Winter Edition focuses on refining existing features rather than introducing new ones, "The Boring Edition" - CLI improvements aim to enhance the developer experience significantly. - GraphQL API enhancements allow for better theme management and integration. - Community feedback is crucial for product development at Shopify. - Ben's journey to Shopify involved building his own company first. - The CLI has been rebuilt to improve functionality and ease of use. - GraphQL is now fully integrated for managing themes and other resources. - Ben encourages developers to build apps using Shopify's public APIs. - The future of Shopify includes more extensibility and community-driven tools. The goal is to enhance the developer experience with tools like VS Code. - Streamlining Liquid development is crucial for efficiency. - Future improvements will focus on making Liquid more expressive and simpler. - Community engagement is vital for the evolution of Liquid. - AI tools like Copilot can significantly impact development workflows. - The integration of various tools can create a seamless experience for developers. - Liquid's evolution aims to maintain simplicity while adding functionality. - Building in public fosters transparency and collaboration. - The developer experience (DX) is directly tied to user experience (UX). - Hot reloading and better asset management are key future features. *Timestamps* 00:00 Ben's Journey to Shopify and Product Role 08:21 Winter Editions Overview and New Features 25:40 Embracing GraphQL for Enhanced API Management 45:12 Building a Strong Foundation for Future Development 50:47 Aligning Developer and Business Goals 56:34 Community Engagement and Open Source Development 01:09:17 Philosophical Insights on Development and Collaboration *Find Ben Online* Twitter(X): https://x.com/benjaminsehl LinkedIn: https://www.linkedin.com/in/benjaminsehl/ *Resources* Shopify Editions Winter '25: https://www.shopify.com/editions/winter2025 KOTN: https://kotn.com/ Sanity.io Groq: https://www.sanity.io/docs/groq Liquid RFCs: https://github.com/Shopify/liquid/discussions/categories/requests-for-suggestions Jeffrey Guenther Shopkeeper: https://github.com/TheBeyondGroup/shopkeeper Vite Plugin for Shopify Dev: https://github.com/barrel/shopify-vite *Picks of the Week* Ben: - Dami Dina AI Generator for Liquid sections https://x.com/DamiDina/status/1861755659353542741 - Teenage Engineering CM-15 https://teenage.engineering/store/cm-15 Karl: Fresca https://www.coca-cola.com/us/en/brands/fresca-sparkling-soda Taylor: Dev Duck https://shopify.supply/products/rubber-duck Signup for Liquid Weekly Don't miss out on expert insights and tips—subscribe to Liquid Weekly for more content like this. https://liquidweekly.com/

Joël and Stephanie go back to fundamentals as they pick apart some recent conversations they've been having around the office. Together they discuss the advantages of GraphQL over a REST API, how they utilise JSONB over a regular column or table, and the use-cases for and against a frontend framework like React. But what's the theme that ties all these conversations together? — The article mentioned in this episode was Why I'm over GraphQL (https://bessey.dev/blog/2024/05/24/why-im-over-graphql/) Your hosts for this episode have been thoughtbot's own Stephanie Minn and Joël Quenneville (https://www.linkedin.com/in/joel-quenneville-96b18b58/). If you would like to support the show, head over to our GitHub page (https://github.com/sponsors/thoughtbot), or check out our website (https://bikeshed.thoughtbot.com). Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot (https://thoughtbot.com/) podcast. Stay up to date by following us on social media - LinkedIn (https://www.linkedin.com/company/150727/) - Mastodon (https://thoughtbot.social/@thoughtbot) - Instagram (https://www.instagram.com/thoughtbot/) © 2024 thoughtbot, inc.

Austin Story, Senior Engineering Director at Doximity, joins Robby to explore the intricacies of building maintainable systems, fostering team accountability, and enabling faster iteration without sacrificing quality. Austin shares how his team approached migrating from a monolithic GraphQL architecture to a federated model, why simplicity matters for long-term success, and how guiding principles like YAGNI influence his decision-making.Doximity is a leading digital platform for medical professionals, and their technology blog offers deep dives into the systems and tools that power their innovative solutions.Key Topics Discussed[00:00:41] What is maintainable software? Austin highlights key traits, including testability, simplicity, and ease of removal.[00:02:09] Designing for removability: Why it's important and how it enables iterative progress.[00:03:05] YAGNI (You Aren't Gonna Need It): How this principle shapes Austin's approach to feature development.[00:04:13] Migrating to GraphQL Federation: Benefits of breaking up a monolithic GraphQL server and the challenges faced during the transition.[00:05:56] GraphQL vs. REST: How GraphQL aids developer productivity while maintaining backward compatibility.[00:10:53] Collaboration between data and application teams: Using tools like Kafka to bridge gaps and improve workflow.[00:17:00] Upgrading Ruby on Rails applications: Balancing autonomy with central guidance for seamless updates.[00:27:55] Fostering ownership on teams: The cultural practices that empower engineers to take initiative and drive results.[00:34:29] Prioritizing work effectively: How Austin's team uses quarterly planning and measurable "goalposts" to align efforts with impact.[00:40:00] Avoiding bike-shedding: Keeping meetings and reviews focused on meaningful progress.Key TakeawaysSimplicity Wins: Maintainable software is easier to adapt, remove, and iterate on when it's kept simple.Iterate and Refine: Use principles like YAGNI to avoid over-engineering and ensure systems are built to evolve.Collaboration Drives Success: Bridging communication between specialized teams can unlock untapped potential.Focus on Outcomes: Define clear goals and track measurable results to ensure projects align with business needs.Resources MentionedYAGNI (You Aren't Gonna Need It)GraphQL Federation OverviewDoximity Technology BlogThe Mom Test by Rob FitzpatrickAustin Story on LinkedInAustin Story's WebsiteStay ConnectedFollow Austin:LinkedInWebsiteThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

In this episode, Bill Kennedy interviews Tanmai Gopal, co-founder and CEO of Hasura, discussing the evolution of San Francisco post-pandemic, the innovative approach of Hasura, and the importance of data security and access. Tanmai shares insights from his academic journey, including his experiences with internships and his master's degree in computer vision, culminating in a fascinating project involving drones. In this conversation, Tanmai Gopal discusses his journey from academia to entrepreneurship, focusing on his experiences in building a consulting business and transitioning to product development. He shares insights on the evolution of GraphQL, the challenges of navigating business decisions, and the future of data access in the context of AI and emerging technologies. The discussion highlights the importance of understanding data modeling and the need for innovative solutions in the software industry.00:00 Introduction03:15 What is Tanmai Doing Today05:45 Understanding Hasura's Approach to APIs14:40 Pre-Hosted Solutions in Hasura22:26 First Memories of a Computer35:40 Favorite Classes During University49:25 From Consulting to Product1:01:35 Extending GraphQL 1:10:30 Competitors of Hasura1:18:40 Data Privacy1:22:10 Contact InfoConnect with Tanmai: Linkedin: https://www.linkedin.com/in/tanmaig/X: https://x.com/tanmaigo?lang=enMentioned in today's episode:Hasura: https://hasura.io/GraphQL: https://graphql.org/Want more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

An airhacks.fm conversation with Phillip Krueger (@phillipkruger) about: early programming experiences with Visual Basic and Java, transition from actuarial science to computer science, first job at a bank working with Java Swing and RMI over CORBA, experience with J2EE and XML technologies, working with XML and XSLT, development of open-source Swing components, work on dotMobi sites for mobile phones in Africa, creation of API extensions for Java EE and MicroProfile, involvement in the MicroProfile GraphQL specification, joining Red Hat and working on quarkus, development of SmallRye GraphQL, improvements to OpenAPI support in Quarkus, work on Quarkus Dev UI, discussion about the evolution of Java application servers and frameworks, comparison of REST and GraphQL, thoughts on Java development culture in South Africa Phillip Krueger on twitter: @phillipkruger

In this episode of the Liquid Weekly Podcast, hosts Karl Meisterheim and Taylor Page welcome Kirill Platonov, a Shopify developer specializing in Ruby on Rails. The conversation explores Kirill's journey into development, his experiences building Shopify apps, and the evolution of the Rails ecosystem. They discuss the challenges and advantages of using Rails with Shopify, the impact of open-source contributions, and the transition to GraphQL. Kirill shares insights on the future of Rails development and the importance of community support in the tech space. Timestamps 00:00 Guest Introduction and Background 02:17 Transitioning to Ruby and Rails 05:12 Building Shopify Apps and Early Experiences 08:03 Challenges with Shopify's Ecosystem 11:00 Developing with Hotwire and AppBridge 14:15 Open Source Contributions and Community Impact 17:10 Working with Shopify's Development Team 20:19 Current Projects and Future Plans 23:21 Reflections on the App Store Landscape 26:11 The Future of Rails in Shopify Development 32:11 Exploring the Full Stack with Rails 37:35 Simplifying App Development with Rails 40:29 Getting Started with Ruby on Rails 43:38 Transitioning to GraphQL 50:30 Updates in the Developer Community 56:22 Personal Updates and Picks of the Week Find Kirill Online Website: https://kirillplatonov.com/ Github: https://github.com/sponsors/kirillplatonov Twitter(X): https://x.com/kirplatonov LinkedIn: https://www.linkedin.com/in/kirplatonov/ Wife's shop: https://bleakandsleek.shop/ Kirill's Apps and Repos Platmart: Bulk Price Editor: https://apps.shopify.com/fast-bulk-price-editor Platmart: Color Swatches: https://apps.shopify.com/fast-product-colors Platmart Size Charts: https://apps.shopify.com/platmart-size-charts Shopify Hotwire Sample: https://github.com/kirillplatonov/shopify-hotwire-sample Polaris View Components: https://github.com/baoagency/polaris_view_components Shopify GraphQL Gem: https://github.com/kirillplatonov/shopify_graphql Resources Shopify App Bridge: https://shopify.dev/docs/api/app-bridge Dev Changelog New .dev community forum: https://community.shopify.dev/ Built for Shopify update to grace period for programmatically assessed criteria: https://shopify.dev/changelog/built-for-shopify-update-to-grace-period-for-programmatically-assessed-criteria Storefront API Cart now supports removing Gift Cards: https://shopify.dev/changelog/storefront-api-cart-now-supports-removing-gift-cards Breaking Changes to CAPTCHA protection on Storefront forms: https://shopify.dev/changelog/breaking-changes-to-captcha-protection-on-storefront-forms New validation against duplicate handles in productCreate, productUpdate, and productSet mutation inputs: https://shopify.dev/changelog/new-validation-against-duplicate-handles-in-productcreate-productupdate-and-productset-mutation-inputs Picks of the Week Kirill: Cursor AI https://www.cursor.com/ Karl: The Mysterious Cities of Gold https://en.wikipedia.org/wiki/The_Mysterious_Cities_of_Gold Taylor: Duolingo https://www.duolingo.com/ Signup for Liquid Weekly Don't miss out on expert insights and tips—subscribe to Liquid Weekly for more content like this. https://liquidweekly.com/

Erik Hedberg besöker återigen Kompilator och hjälper Bartek att reda ut vad GraphQL är _egentligen_.Hostingen av Kompilator sponsras av Dekalfabriken

In this episode, Chris Nowicki shares his path from aerospace to web development and the unique challenges of transitioning into tech. Chris and James discuss how Chris got involved in the open-source project "Deals for Devs," including the tech stack, managing contributions, and handling obstacles. This episode offers a first-hand look at the value of community in development and tips for new devs on getting started in open source.SponsorPostman is an API platform for building and using APIs. Postman simplifies each step of the API lifecycle and streamlines collaboration so you can create better APIs—faster.Show Notes00:00 - Intro01:08 - Chris Nowicki's Journey into Tech02:12 - Bootcamp Experience and Structure05:07 - Breaking into Tech Through Community Involvement08:38 - Deals for Devs: The Project Origin11:10 - Sponsor Message: Postman12:06 - Tech Stack Overview for Deals for Devs13:22 - Tech Stack: Resend, React Email, Tailwind, and Xata17:00 - Prisma Integration with Xata20:00 - Challenges in Managing Community Projects23:54 - Planning and Issue Management for Deals for Devs28:00 - Feature Flags and Release Management37:15 - Subscription System Workflow45:45 - Creating a Dynamic Email Subscription System51:58 - Managing Admin and Approval for Deals52:26 - ClosingLinksOpenSaucedRedwoodJSDeals for Devs ProjectPostmanReact EmailVercelXataResendFrontend MentorLaunchDarklyGrid Iron SurvivorDev.to article on CRON jobs

Join Dan Vega as he explores Spring for GraphQL with special guests Brian Clozel and Rossen Stoyanchev from the Spring team. In this deep-dive episode, the experts discuss the evolution of Spring for GraphQL, its relationship with GraphQL Java, and how it compares to Netflix's DGS framework. Learn about GraphQL Federation, handling N+1 problems with batch loading, and when to choose GraphQL over REST. The conversation covers practical insights on error handling, security considerations, and the future roadmap of Spring for GraphQL.Show Notes:* Origins of Spring for GraphQL and collaboration with GraphQL Java* Use cases for choosing GraphQL in enterprise applications* Federation support and microservices architecture* Batch loading and handling N+1 problems* Error handling in GraphQL vs REST* Spring for GraphQL and Netflix DGS framework integration* Future roadmap with Spring Framework 7* Tips for getting started with Spring for GraphQLJoin the live stream to ask questions or catch the replay on your preferred podcast platform.

Amy Williams from Skellig Automation joins hosts Phil Seboa and Ed Fuentes to dive into the world of industrial IoT and automation in life sciences. Learn about Amy's journey into life sciences, challenges in the pharmaceutical industry, and the potential of digital tools like the Unified Namespace and Industry 4.0. Discover how new technologies are improving data management and making healthcare more accessible. Tune in for an inspiring conversation on the future of life sciences and the digital revolution. 00:00 Introduction and Welcome 00:30 Introduction of Amy Williams 01:45 Amy's Background in Life Sciences and Automation 05:32 Paper vs. Digital Workflow in Pharmaceuticals 08:40 Insights on Graph Databases 10:50 GraphQL and Database Automation 13:00 The Future Landscape of Tech in Life Sciences 15:12 The Role of Biosimilars in Healthcare 17:35 Addressing US Drug Shortages 20:05 Skellig's Initiatives in Supply Chain Digitalization 22:16 Challenges in Technology Adoption 24:40 Amy's Influences and Family Background 27:00 From College to Automation Engineering 30:15 Transitioning to Life Sciences 33:03 Personal Health Journey and its Impact 36:22 Making Healthcare More Accessible 38:40 Inefficiencies in Manufacturing and Costs to Patients 42:10 Advocacy for Industry Change 44:00 Upcoming News on Industry Innovations 46:30 Advice for Embracing Industry 4.0 49:00 Conclusion and Final Thoughts Connect with Amy on LinkedIn: https://www.linkedin.com/in/amy-williams-a8974b114/ Connect with Phil on LinkedIn: https://www.linkedin.com/in/phil-seboa/ Connect with Ed on LinkedIn: https://www.linkedin.com/in/ed-fuentes-2046121a/ About Industry Sage Media: Industry Sage Media is your backstage pass to industry experts and the conversations that are shaping the future of the manufacturing industry. Learn more at: http://www.industrysagemedia.com

Scott and Wes talk with Søren Bramer Schmidt, Founder and CEO of Prisma, about database best practices, including the latest developments in serverless, local-first, and typed SQL solutions. Show Notes 00:00 Welcome to Syntax! 02:55 Søren's thoughts on GraphQL 03:53 Brought to you by Sentry.io 06:57 Common database mistakes 08:52 Prisma's stability and user experience 10:42 Typed SQL and advanced querying Announcing TypedSQL: Make your raw SQL queries type-safe with Prisma ORM Prisma Optimize 20:47 Serverless challenges and solutions Prisma Accelerate 27:11 Cloudflare's potential to dethrone AWS 29:13 Prisma and local-first development Prisma & Expo: A Better Path to Local-First Apps | App.js Conf 2024 35:30 Making local-first development mainstream 40:10 Challenges with async 42:43 Søren's thoughts on Drizzle 43:41 Søren's favorite database 47:21 The read your writes problem 48:58 Prisma hosted Postgres 51:44 Sick Picks & Shameless Plugs Sick Picks Søren: Cursor Shameless Plugs Søren: 1: Prisma Optimize 2: Prisma Postgres (coming soon) Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

GraphQL is an open-source query language for APIs and a runtime for executing those queries. It was developed by Facebook to address the problem of over-fetching or under-fetching data, which is a common issue with traditional REST APIs. Matt Bessey is a Principal Engineer and Software Architect. Earlier this year Matt wrote a blog post The post The End of GraphQL with Matt Bessey appeared first on Software Engineering Daily.

GraphQL is an open-source query language for APIs and a runtime for executing those queries. It was developed by Facebook to address the problem of over-fetching or under-fetching data, which is a common issue with traditional REST APIs. Matt Bessey is a Principal Engineer and Software Architect. Earlier this year Matt wrote a blog post The post The End of GraphQL with Matt Bessey appeared first on Software Engineering Daily.

Kent C. Dodds, web dev educator, discusses the evolution of web architectures, the potential of React Server Components, and the latest advancements in React 19, offering insights perfect for developers eager to stay ahead. Links https://kentcdodds.com https://x.com/kentcdodds https://github.com/kentcdodds https://www.youtube.com/c/KentCDodds-vids https://www.linkedin.com/in/kentcdodds https://www.epicreact.dev https://www.testingjavascript.com https://www.epicweb.dev We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Kent C. Dodds.

In this episode of Breaking Changes, Postman Head of Product-Observability Jean Yang sits down with Nick Schrock, the co-creator of GraphQL, to dive into the fascinating journey behind GraphQL's development. They discuss how GraphQL transitioned from an internal system at Facebook to a widely adopted technology—as well as how Nick's newest venture, Dagster Labs, is revolutionizing data orchestration with asset-oriented pipelines. This conversation dives deep into the realm of data engineering and its transformative potential for businesses. Nick and Jean also share insights into the intersection of AI and software engineering, and Nick offers his perspective on responsible AI development.   For more on Nick Schrock, check out the following: LinkedIn: https://www.linkedin.com/in/schrockn/ Twitter: https://twitter.com/schrockn GraphQL Website: https://graphql.org/   Follow Jean on Twitter/X @jeanqasaur. And remember, never miss an episode by subscribing to the Breaking Changes Podcast on your favorite streaming platform or Postman's YouTube Channel—just hit that bell for notifications. #BreakingChanges #data #postman #grahpql #TechLeadership #ai #podcast

 In today's episode, Charles, Steve, and AJ, are joined by back-end engineer and team lead at Homebound, Stephen Haberman. We delve into the fascinating world of SQL c and its revolutionary approach to managing SQL queries with dedicated SQL files, delivering benefits such as reduced typing errors and pre-deployment checks. Stephen also walks us through the advantages and limitations of ORMs versus query builders like Prisma and Drizzle, sharing insights into Joyce ORM's unique philosophy and simplified CRUD operations.They explore the intricacies of Domain Driven Design (DDD), its emphasis on ubiquitous language, and how it shapes business logic and storage management. AJ contributes by discussing the potential of SQL c and Slonik for dynamic query building. Additionally, they discuss Steven's innovative work with GraphFileWorker and GrafAST, highlighting the performance improvements in GraphQL backends. Whether you're intrigued by the technicalities of ORMs, the evolution of database tools, or just love a good anecdote, this episode packed with technical insights and lively discussions is one you won't want to miss. Join them on this journey into the world of database management and development!SocialsLinkedIn: Stephen HabermanPicks AJ - TypeScript to JSDocAJ - MySQL to TypeScriptAJ - sqlcAJ - Slonik (Node + Postgres)AJ - SwiftUI EssentialsAJ - Introduction to SwiftUI AJ - Trump, but not saying dumb thingsCharles - Biblios | Board GameCharles - FreeStyle Libre 3 System | Continuous Glucose MonitoringStephen - Grafast | GrafastBecome a supporter of this podcast: https://www.spreaker.com/podcast/javascript-jabber--6102064/support.

Tom Occhino, Chief Product Officer at Vercel and former Engineering Director at Facebook, joins Sam to talk about the pivotal moments in React's history. He talks about how React popularized the ideas of declarative rendering and unidirectional data flow, how GraphQL furthered React's goal of co-locating all the concerns of a particular piece of UI, the problems that GraphQL led to at Facebook and how Relay solved them, and how Suspense, Server Components, and PPR are the generalized spiritual successors to the stack used at Facebook.Timestamps:0:00 - Intro2:53 - Declarative rendering as React's legacy8:12 - How GraphQL enabled complex components to be fully self-contained20:12 - How React's goal has always been to co-locate all the concerns of a particular piece of UI22:58 - The problem with co-locating GraphQL with components, and how Relay solved it26:28 - How RSC is the generalized spiritual successor to BigPipe and GraphQL34:46 - What PPR is, and how it and Suspense fit into this story55:55 - The general paradigm shift of getting static code to the device as soon as possibleLinks:Tom Occhino with Ben DunphyReact: The DocumentaryReact Roundtable with Andrew Clark and Sebastian MarkbågeTom Occhino on Twitter

Guest Brian Douglas Panelist Richard Littauer Show Notes In this episode of Sustain, host Richard Littauer talks with Brian “bdougie” Douglas, founder and CEO of Open Sauced. They discuss the multifaceted aspects of sustaining open source projects, Brian's journey in developer advocacy, and the unique goals of Open Sauced. Brian shares insights from his experiences at GitHub and Netlify, elaborates on concepts like lottery factor and the significance of unique issue authors, and tackles the challenges of maintaining open source sustainability. He also explores the balance of addressing enterprise needs while supporting smaller, less visible projects and emphasizes the importance of education and community engagement in open source. Press download now! [00:01:54] Brian discusses his background at GitHub and Netlify, his role in promoting GraphQL, GitHub Actions, Codespaces, and the inception of Open Sauced. [00:03:08] We hear about the features of Open Sauced's dashboard which enhances GitHub insights, OSSF scorecards, and workspace customizations for managing multiple projects. [00:04:31] Open Sauced's business model is currently founded by VC money and aims to serve large organizations with significant open source dependencies, and Brian talks about the team size and funding history. [00:06:08] Brian elaborates on Open Sauced's long-term sustainability plan, focusing on enterprise-level solutions for open source project observability and contributions. [00:09:31] There's a discussion on how Open Sauced interacts with open source communities and the importance of real-world testing and contributions to open source projects. [00:11:06] Richard highlights the FOSS Funders initiative, encouraging companies to support open source projects financially and through active participation. [00:12:44] Brian shares insights on effective metrics for evaluating open source projects, emphasizing the importance of engaging with unique issue authors rather than focusing solely on superficial metrics like pull requests, and discusses his approach to starting meaningful conversations in the open source community. [00:16:08] Brian explains why he renamed “Lottery Factor” to “Contributor Absence Factor,” and discusses the Pgvector project to illustrate the importance of understanding the “Contributor Absence Factor” and the sustainability concerns when a project relies heavily on a few contributors. [00:18:20] We learn more about how Open Sauced sources its data, including their use of GitHub's events feed and their development of the “Pizza Oven” tool to generate insights from Git repositories. [00:20:21] Richard and Brian discuss the challenges of maintaining an open source ethos when dealing with large companies' internal projects, avoiding becoming merely service providers for large corporate entities. [00:24:14] Brian discusses the long-term implications of open source projects that receive substantial funding or become integrated into larger corporate frameworks. [00:27:27] Richard brings up the difficulty many open source projects face in accessing significant funding and Brian shares his vision for supporting less prominent open source projects drawing analogies from his personal experiences. [00:32:42] Richard questions the “up the chain” analogy, comparing it to a pyramid scheme or academia's tenure track. Brian acknowledges the need to support contributors at all levels, not just those at the top, and he introduces the concept of a S Bomb to provide transparency about project dependencies. [00:39:36] Find out where you can follow Brian on the web. Spotlight [00:40:17] Richard's spotlight is Mr. Carreras, an awesome music teacher. [00:40:59] Brian's spotlight is Dawn Foster at the CHAOSS Project and the CHAOSS Practitioner Guides. Links SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (email) (mailto:podcast@sustainoss.org) richard@theuserismymom.com (email) (mailto:richard@theuserismymom.com) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Brian Douglas- Open Sauced (https://app.opensauced.pizza/u/bdougie) Brian Douglas Website (https://b.dougie.dev/) Brian Douglas GitHub (https://github.com/bdougie) Brian Douglas X/Twitter (https://github.com/bdougie) The Secret Sauce Open Sauced Podcast (https://podcasts.apple.com/us/podcast/the-secret-sauce/id1644263270) The Secret Sauce Podcast: ‘The Future of Cloud Native and AI with Brendan Burns' (https://podcasts.apple.com/fr/podcast/the-future-of-cloud-native-and-ai-with-brendan-burns/id1644263270?i=1000658092259) Open Sauced (https://opensauced.pizza/) Renaming Bus Factor #632 (CHAOSS community) (https://github.com/chaoss/community/issues/632#issuecomment-2152929617) FOSS Funders (https://fossfunders.com/) Andrew Kane GitHub (https://github.com/ankane) Chad Whitacre Website (https://chadwhitacre.com/) Fair Source (https://fair.io/) CHAOSS (https://chaoss.community/) Your Copilot for Git History (Open Sauced) (https://opensauced.pizza/docs/features/star-search/) Open Sauced GitHub (https://github.com/open-sauced/pizza) InnerSource Commons (https://innersourcecommons.org/) Sustain Podcast-Episode 148: Ali Nehzat of thanks.dev and OSS Funding (https://podcast.sustainoss.org/148) Learning in Public with Kelsey Hightower (Curiefense) (https://www.curiefense.io/blog/learning-in-public-with-kelsey-hightower/) Welcome to Wrexham (https://en.wikipedia.org/wiki/Welcome_to_Wrexham) Sustain Podcast-Episode 159: Dawn Foster & Andrew Nesbitt at State of Open Con 2023 (https://podcast.sustainoss.org/guests/foster) Dr. Dawn Foster Mastodon (https://hachyderm.io/@geekygirldawn) About the CHAOSS Practitioner Guides (https://chaoss.community/about-chaoss-practitioner-guides/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guest: Brian Douglas.

Erik Hanchett, senior developer advocate at AWS Amplify, explores the world of Fullstack TypeScript. He discusses the significance of end-to-end type safety, the tools to achieve it, and delves into the benefits and functionalities of AWS Amplify. Links https://www.programwitherik.com https://x.com/erikch https://www.youtube.com/c/programwitherik https://www.linkedin.com/in/erikhanchett https://trpc.io https://orval.dev https://docs.amplify.aws We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Erik Hanchett.

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django's battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

In episode 800 of Syntax, Scott and Wes sit down with John Resig, the creator of jQuery, to discuss the current state of React and TypeScript. They dive into the evolution of frontend frameworks, the challenges of server-side rendering, and the tech stack at Khan Academy. Show Notes 00:00 Welcome to Syntax! 00:59 Brought to you by Sentry.io. 01:32 What is jQuery? 05:31 Did you anticipate the success jQuery had? 07:16 allow-discrete, @starting-style. Install Nothing: App UIs With Native Browser APIs - Scott Tolinski. 07:54 Building the community around jQuery. 11:16 jQuery plugins. 13:00 Did you ever make money from jQuery? 16:13 What is your role at Khan Academy. 17:58 What is the tech stack at Khan Academy? 21:56 Why do you want to change your CSS and JS framework? 24:03 TypeScript vs Flow. 25:25 GraphQL federation. 28:08 What was your frontend framework journey? 30:23 Is there any part of React you wish would improve? 32:37 Reservations using React Router. 33:14 Khan Academy web platform vs native platform. 35:21 What do you use for state management? 38:48 What's harder than it should be on the web today? Kilian's Question On X. Polypane.app. 42:46 Opinions on JavaScript Sprinkles. 44:04 What's with the $ sign in jQuery? 45:29 The challenges of having your name in such a widely used software. 51:06 Challenges with server-side rendering in React. 52:42 Sick Picks & Shameless Plugs. 54:48 What are the performance issues associated with internationalization? 56:57 Back to Sick Picks & Shameless Plugs. Sick Picks John: Biome, Remix, Lingui. Shameless Plugs John: Khan Academy. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

David Flanagan, founder of Rawdoke Academy, discusses why WebAssembly (WASM) could be the future of serverless technology and explores the evolution, benefits, and potential of WASM in transforming server-side applications across various environments. Links https://davidflanagan.com https://github.com/davidflanagan https://twitter.com/__DavidFlanagan https://www.linkedin.com/in/rawkode https://rawkode.academy https://youtube.com/@RawkodeAcademy https://www.hopp.bio/rawkode We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: David Flanagan.

In this episode of Maintainable, Robby Russell sits down with Tanmai Gopal, the CEO and co-founder of Hasura. Tanmai shares his insights into the characteristics of well-maintained software and the importance of a codebase that no team member fears. He emphasizes the need for accessibility and understandability in code, making it easier for developers to work with and iterate upon.Tanmai dives deep into the metaphor of technical debt, urging teams to prioritize product outcomes over best practices. He highlights the value of addressing technical debt contextually and in a way that aligns with product goals.A significant portion of the discussion revolves around the concept of the "super graph" in GraphQL. Tanmai explains how a unified API, created through federated GraphQL, can streamline API integration and reduce latency. He compares GraphQL with RESTful APIs, showcasing the advantages of a graph-based approach for handling complex data relationships.Tanmai also introduces Hasura's platform, which introspects databases, code, and APIs to create a comprehensive super graph. This platform simplifies API management, making it easier for developers to maintain and evolve their applications.Listeners will also learn about Hasura's upcoming user conference and the new features they plan to unveil. Tanmai shares his top science fiction book recommendations and where to follow his thoughts on software engineering online.Key Takeaways:The importance of a fearless codebase for well-maintained software.Strategies to improve code accessibility and understandability.The metaphor of technical debt and its contextual importance.The concept and benefits of a super graph in GraphQL.How Hasura simplifies API management through introspection.Upcoming Hasura user conference and new features.Resources Mentioned:HasuraTanmai Gopal on LinkedInN.K. Jemisin's Broken Earth TrilogyNaomi Novik's UprootedMartha Wells' Murderbot DiariesThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and soon, other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

Naoki Hiroshima さんをゲストに迎えて、米最高裁、大統領選、叡王戦、Apple, ローカライゼーションなどについて話しました。 Show Notes Supreme Court overrules Chevron, kneecapping federal regulators Could Democrats Replace Biden as their nominee? Election 2024: Trump proposes green cards for foreign grads of US colleges McCain Counters Obama 'Arab' Question Trump's Second Term: Last Week Tonight with John Oliver There's a 16-Year Old, 7'3" Chinese Women's Basketball Player Caitlin Clark ハイキュー!! 叡王戦 KADOKAWA広報: 当社へのランサムウェア攻撃による情報漏洩に関して Apple is first company charged with violating EU's DMA rules Apple Intelligence features probably won't launch in the EU in 2024 “a SQL” or “an SQL”? Why, after 6 years, I'm over GraphQL

Summary Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou Interview Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution? What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges? How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine? What are the challenges in terms of safety and reliability? What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics? Contact Info LinkedIn (https://www.linkedin.com/in/diptiborkar/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Microsoft Fabric (https://www.microsoft.com/microsoft-fabric) Ahana episode (https://www.dataengineeringpodcast.com/ahana-presto-cloud-data-lake-episode-217) DB2 Distributed (https://www.ibm.com/docs/en/db2/11.5?topic=managers-designing-distributed-databases) Spark (https://spark.apache.org/) Presto (https://prestodb.io/) Azure Data (https://azure.microsoft.com/en-us/products#analytics) MAD Landscape (https://mattturck.com/mad2024/) Podcast Episode (https://www.dataengineeringpodcast.com/mad-landscape-2023-data-infrastructure-episode-369) ML Podcast Episode (https://www.themachinelearningpodcast.com/mad-landscape-2023-ml-ai-episode-21) Tableau (https://www.tableau.com/) dbt (https://www.getdbt.com/) Medallion Architecture (https://dataengineering.wiki/Concepts/Medallion+Architecture) Microsoft Onelake (https://learn.microsoft.com/fabric/onelake/onelake-overview) ORC (https://orc.apache.org/) Parquet (https://parquet.incubator.apache.org) Avro (https://avro.apache.org/) Delta Lake (https://delta.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) Hadoop (https://hadoop.apache.org/) PowerBI (https://www.microsoft.com/power-platform/products/power-bi) Podcast Episode (https://www.dataengineeringpodcast.com/power-bi-business-intelligence-episode-154) Velox (https://velox-lib.io/) Gluten (https://gluten.apache.org/) Apache XTable (https://xtable.apache.org/) GraphQL (https://graphql.org/) Formula 1 (https://www.formula1.com/) McLaren (https://www.mclaren.com/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

A popular open source iOS authenticator app goes rogue under new ownership, Andreas Kling steps back from SerenityOS & forks Ladybird, Vhyrro takes a thought-provoking try at a “static effect system”, Matt Bessey is over GraphQL & Marc-Andre Giroux still likes GraphQL sometimes (in the right context).

Welcome to episode 500 of the TestGuild Automation Podcast! Today, we're diving deep into contract testing with our expert speakers, Marie Cruz and Lewis Prescott. Listen in to discover the challenges and innovative solutions for introducing contract testing for public and third-party APIs, where control is often limited. Marie and Lewis share their insights on the provider-driven and bidirectional contract testing approaches, emphasizing the importance of human communication between teams alongside automated tests. We also take a sneak peek into their book, "Contract Testing in Action," packed with practical guidance and now available with a special 40% discount until August 24th. Whether you're dealing with web, mobile, GraphQL, or event-driven services, this episode covers implementing contract testing across different types, integrating it into your CI pipeline, and the strategic shift from traditional integration tests to contract tests for early and reliable feedback. Join us as we uncover the intricacies of contract testing, tools like Pact and PactFlow, and the best practices for making it part of your development workflow. Take advantage of valuable insights and real-world examples from two of the industry's leading experts, and learn how to elevate your testing strategy to ensure seamless, bug-free software releases.

What if instead of sending multiple queries out to APIs and getting disparate data back, you could just send a single query and receive a single answer. That's exactly what GraphQL does for you. Rick Donato joins the show today to teach us about GraphQL and how it can help us on the path to... Read more »